Epistemic uncertainty [1]}, due to limited training data, is a universal problem for automatic speech recognition (ASR), and machine learning in general.
Transcribed data is difficult to obtain because it is either expensive or restricted due to privacy or proprietary reasons.
Some companies do have access to tremendous amounts of transcribed data, but it cannot be shared beyond the company.
It is also possible to obtain large amounts of transcribed data in certain domains like read speech [2]}, but that data is unlikely to be useful in building models for more difficult domains like far-field and conversational speech.
While gathering large quantities of labeled data is difficult, unlabeled data is easier to find.
Assuming the model's purpose is to process large amounts of data, then this same data can serve as untranscribed training data.
Even if a human is not allowed to inspect the data for privacy or security reasons, an automated system may still use the data for training.
